Previously 3D pose estimation on humans was proposed by Martinez [1]} where a simple linear residual network was trained on HumanEva and Human3.6M dataset. This architecture has already been evaluated by Gosztolai [2]} for 3D pose estimation in freely moving monkeys as well as rodents in a constrained behavioral setup. Pose lifting of human data using temporal 1-dimensional dilated convolutional neural networks was previously proposed by Pavllo [3]}. View-invariant human pose estimation from embedding spaces was recently proposed by Sun [4]}.
Approaches to triangulation of animals include Anipose [5]} and more recently the DANNCE framework that employs 3D convolutions for improving detections of 3D poses captured from a multi-camera setup [6]}.
